This is an example of what the automated proteomics analysis pipeline
outputs, using all synthetic dummy data as an example. Typically, a text
file is read in that details the scope of work, sample prep, and data
acquisition - which has all been omitted for this example. This is
generally used for core reports but also used to start research studies
as a baseline analysis.
Sample Type: Water
Sample #: 00
Type of analysis: Proteomics
Instrument: Instrument 1
2. Sample Preparation
Protein was digested using….
Peptides were processed using…
3. Data Acquisition
MS data were acquired on…
Peptides were directly loaded on…
MS settings:
MS1 scans…
MS2 scans…
Only precursors with…
Resampling of same precursors was…
4. Search Parameters
Global proteomics data were searched using…
Digestion
Peptide modifications
Missed cleavages
FDR threshold
5. Samples
The following table was taken directly from the sample submisison sheet,
detailing the samples included in this study.
Sample ID
Sample Type
Species
Condition 1
Condition 2
1
Tissue
Human
Vehicle
Test
2
Tissue
Human
Vehicle
Test
3
Tissue
Human
1mg
Test
4
Tissue
Human
1mg
Test
5
Tissue
Human
1mg
Test
6
Tissue
Human
5mg
Test
7
Tissue
Human
5mg
Test
8
Tissue
Human
5mg
Test
9
Tissue
Human
Vehicle
Test
10
Tissue
Human
Vehicle
Test
11
Tissue
Human
Vehicle
Control
12
Tissue
Human
Vehicle
Control
13
Tissue
Human
1mg
Control
14
Tissue
Human
1mg
Control
15
Tissue
Human
1mg
Control
16
Tissue
Human
5mg
Control
17
Tissue
Human
5mg
Control
18
Tissue
Human
Vehicle
Control
19
Tissue
Human
Vehicle
Control
6. Quality Control
Samples were bracketed by E. coli QC runs, which were then correlated
to ensure instrument quality. QC passed threshold (>= 0.9) with an
average R^2 of 0.98
TEV Protein Spike
TEV protein has been spiked into all samples as a quality control
measure to ensure consistency in sample preparation and instrument
performance across all samples.
7. Data Missingness
The bar plot below shows the percent missingness in each sample
relative to the total amount of unique proteins identified across all
samples. Missingness is expected, particularly when working with
different conditions between samples. Any sample with more than 50%
missingness is flagged.
8. Protein Identification
9. Data Analysis
Unsupervised clustering of protein abundances
Heatmap
PCA Plots
Unique Proteins
The upset plot below visualizes the proteins intersecting across
different conditions, highlighting unique proteins identified within the
groups.
Differentially Abundant Proteins
Volcano Plots
Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 5mg_PS19 and Vehicle_PS19 (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
108 proteins are significantly increased in 5mg_PS19
125 proteins are significantly decreased in 5mg_PS19
Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 1mg_PS19 and Vehicle_PS19 (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
108 proteins are significantly increased in 1mg_PS19
125 proteins are significantly decreased in 1mg_PS19
Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 5mg_PS19 and 1mg_PS19 (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
108 proteins are significantly increased in 5mg_PS19
125 proteins are significantly decreased in 5mg_PS19
Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between Vehicle_PS19 and Vehicle_NonTg (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
108 proteins are significantly increased in Vehicle_PS19
125 proteins are significantly decreased in Vehicle_PS19
Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 5mg_PS19 and Vehicle_NonTg (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
108 proteins are significantly increased in 5mg_PS19
125 proteins are significantly decreased in 5mg_PS19
Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 1mg_PS19 and Vehicle_NonTg (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
108 proteins are significantly increased in 1mg_PS19
125 proteins are significantly decreased in 1mg_PS19
Acknowledgements
IMS Acknowledgments & Co-authorship
Guidelines
All work performed at the Integrated Mass Spectrometry (IMS) at City
of Hope’s Comprehensive Cancer Center should be acknowledged in
scholarly reports, presentations, posters, papers, and all other
publications. Proper acknowledgment provides a visible measure of the
impact of the City of Hope’s shared resources and is essential for our
continued funding.
When to Acknowledge or Provide Co-Authorship
Include an acknowledgement any time IMS provides services that
support your research.
If staff members have made a significant intellectual contribution
beyond routine sample analysis, co-authorship is expected. We determine
co-authorship based on authorship guidelines published by the
Association of Biomolecular Resource Facilities (ABRF).
Format for Co-Author Affiliations
Please acknowledge staff members as “Integrated Mass
Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center,
Duarte, CA”
Format for Manuscript Acknowledgments
Include the following statement, as required by the NCI:
“We acknowledge the support of the IMS at City of Hope Comprehensive
Cancer Center supported by the National Cancer Institute of the National
Institutes of Health under award number P30CA33572.”
Notify Facility of Acknowledgement
Please notify IMS when your scholarly report, presentation, poster,
or paper containing a facility acknowledgement is published. Accurately
quantifying the impact of our facility helps to keep the facility open
and available to collaborators.
NIH and NSF Grant Attribution
Please connect with the facility director
for language on how our facilities may be described in grant proposals
to both the National Institutes of Health and the National Science
Foundation.
Data Retention
Raw and processed data are securely stored and retained for 30
calendar days post-project completion. Collaborators will be provided
with a downloadable link for data access within this period after which
data will be archived. Archived data may be retrieved up to seven years,
for a fee.